Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

Nonfiction, Science & Nature, Technology, Automation, Electronics
Cover of the book Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques by Silvio Simani, Cesare Fantuzzi, Ron J. Patton, Springer London
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Author: Silvio Simani, Cesare Fantuzzi, Ron J. Patton ISBN: 9781447138297
Publisher: Springer London Publication: November 11, 2013
Imprint: Springer Language: English
Author: Silvio Simani, Cesare Fantuzzi, Ron J. Patton
ISBN: 9781447138297
Publisher: Springer London
Publication: November 11, 2013
Imprint: Springer
Language: English

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

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Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

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